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EAGER-QAC-QCH: Hybrid Quantum Classical Algortithm for NMR Inference

EAGER-QAC-QCH：用于 NMR 推理的混合量子经典算法

基本信息

批准号：
2037687
负责人：
Arthur Jaffe
金额：
$ 30万
依托单位：
Harvard University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2037687&HistoricalAwards=false
关键词：
EAGER QAC QCH Hybrid Quantum

项目摘要

Eugene Demler of Harvard University is supported by an EAGER award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to study Hybrid Quantum-Classical Algorithms for NMR Inference. The Condensed Matter and Materials program in the Division of Materials Research also cofunds this award. The proposal was submitted in response to the Quantum Algorithm Challenge Dear Colleague Letter, NSF 20-056. NMR is one of the most powerful analytical techniques available to medicine and biology. It is suited for both in vivo and in vitro studies. Yet it is difficult to interpret the data in NMR experiments. Spectral profiling of compounds is a complex pattern recognition problem. Spectroscopic analysis and interpretation for NMR compound identification is cumbersome and slow. It can be inconsistent across platforms. It is generally hard to scale it to novel compounds, an important obstacle to drug discovery and identification of mechanism of action. Therefore, compound identification is a major challenge for implementing these technologies in medicine, engineering, and science. Quantum correlations and entanglement rather than traditional correlations define NMR spectra. Therefore, quantum approaches to solve spectroscopic inference problems are well-suited to achieve a sought-after quantum advantage. Eugene Demler is developing hybrid approaches that combine the tools of data science, deep learning methods, with quantum computing to address the problem of inference of spectral analysis. This work can have a wide impact in many applications of NMR in medicine, science and engineering, with an immediate application in metabolomics compound identification, which focuses on profiling small molecules inside cells, organs and body fluids. NMR is one of the most powerful analytical techniques available to medicine and biology, as it is suited for both in vivo and in vitro studies. Yet it is difficult to interpret the data in NMR experiments. One directly observes only the magnetic spectrum of a biological sample, whereas the ultimate goal is to learn about the underlying microscopic Hamiltonian and ultimately identify and quantify chemical compounds. Eugene Demler is developing a hybrid approach that combines quantum computing, quantum simulations, and classical machine learning to address the problem of NMR inference. The novelty of this approach is in using quantum simulators to compute spectra for hypothetical values of the Hamiltonians, and then using classical deep-learning algorithms to optimize these parameters. Eugene Demler’s work address the following specific questions: (i) Finding optimal protocols for sampling NMR spectra on currently available quantum computing platforms with a focus on ion chains and Rydberg arrays; (ii) improving hybrid algorithms through finding more efficient methods of Trotterizing the Hamiltonian evolution; (iii) improving the classical optimization procedure using LASSO regularization of the variational Bayesian Gaussian method; (iv) establishing the theoretical limits to quantum assisted NMR inference ; (v) collaborating with experimental groups to provide a proof of principle experimental demonstration of quantum assisted NMR inference.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

哈佛大学的尤金德姆勒获得了化学系化学理论、模型和计算方法项目的EAGER奖，以研究NMR推断的混合量子经典算法。材料研究部的凝聚态物质和材料计划也共同资助了这个奖项。该提案是为了回应量子算法挑战亲爱的同事信，NSF 20-056。NMR是医学和生物学中最强大的分析技术之一。它适用于体内和体外研究。然而，在核磁共振实验中解释数据是困难的。化合物的光谱分析是一个复杂的模式识别问题。用于NMR化合物鉴定的光谱分析和解释是繁琐且缓慢的。它可能在不同平台上不一致。一般很难将其规模化为新化合物，这是药物发现和确定作用机制的重要障碍。因此，化合物鉴定是在医学、工程和科学中实施这些技术的主要挑战。量子关联和纠缠，而不是传统的相关定义NMR光谱。因此，解决光谱推断问题的量子方法非常适合实现广受欢迎的量子优势。尤金Demler正在开发混合方法，将数据科学工具、深度学习方法与量子计算相结合，以解决光谱分析的推理问题。这项工作可以在NMR在医学，科学和工程中的许多应用中产生广泛的影响，并立即应用于代谢组学化合物鉴定，其重点是分析细胞，器官和体液内的小分子。NMR是医学和生物学中最强大的分析技术之一，因为它适用于体内和体外研究。然而，在核磁共振实验中解释数据是困难的。人们只直接观察生物样品的磁谱，而最终目标是了解潜在的微观哈密顿量，并最终识别和量化化合物。尤金德姆勒正在开发一种混合方法，结合量子计算，量子模拟和经典机器学习来解决NMR推理的问题。这种方法的新奇在于使用量子模拟器来计算哈密顿量的假设值的谱，然后使用经典的深度学习算法来优化这些参数。尤金Demler的工作解决了以下具体问题：（i）在当前可用的量子计算平台上寻找NMR光谱采样的最佳协议，重点是离子链和Rydberg阵列;（ii）通过寻找更有效的Trotterizing Hamilton演化方法来改进混合算法;（iii）使用贝叶斯变分高斯方法的LASSO正则化来改进经典优化过程;（iv）在现有的量子计算平台上寻找最佳方案。（iv）建立量子辅助核磁共振推断的理论极限;（v）与实验小组合作，提供量子辅助核磁共振推断的原理实验演示证明。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。